Data, learning algorithms and models are examined for unwanted bias capable of causing unintended harm, which include physical and psychological harm and discrimination against protected characteristics. Wherever harm could result, strategies to monitor and mitigate it are developed with sufficient understanding of the AI behaviour, and even deliberate bias has its ethical harms weighed and mitigated.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.